{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Simple Linear Regression Part 2"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Linear Regression is the simplest form of regression. The dependent variable is continuous; however, the relationship between the dependent variable and indepedent variables is assumed to be linear. \n",
        "\n",
        "# Equation: y = mx + b\n",
        "\n",
        "y: dependent variable (target)   \n",
        "x: independent variable (predictor)\n",
        "m: constant and slope or gradient   \n",
        "b: intercept\n",
        "\n\n",
        "1. There must be a linear relation between independent and dependent variables. \n",
        "2. There should not be any outliers present. \n",
        "3. No heteroscedasticity \n",
        "4. Sample observations should be independent. \n",
        "5. Error terms should be normally distributed with mean 0 and constant variance. \n",
        "6. Absence of multicollinearity and auto-correlation.\n",
        "\n\n\nClassification trees have dependent variables that are categorical or label and unordered. Example: Pass or Fail. Regression trees have dependent variables that are continuous values or ordered whole values. Example; Percentages. Regression means to predict the output value using training data. Classification means to group the output into a class. "
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Preprocessing"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# fix_yahoo_finance is used to fetch data \n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 14,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2018-08-27'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/plain": [
              "            Open  High   Low  Close  Adj Close    Volume\n",
              "Date                                                    \n",
              "2014-01-02  3.85  3.98  3.84   3.95       3.95  20548400\n",
              "2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200\n",
              "2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300\n",
              "2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100\n",
              "2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>3.85</td>\n",
              "      <td>3.98</td>\n",
              "      <td>3.84</td>\n",
              "      <td>3.95</td>\n",
              "      <td>3.95</td>\n",
              "      <td>20548400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>3.98</td>\n",
              "      <td>4.00</td>\n",
              "      <td>3.88</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>22887200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>4.01</td>\n",
              "      <td>4.18</td>\n",
              "      <td>3.99</td>\n",
              "      <td>4.13</td>\n",
              "      <td>4.13</td>\n",
              "      <td>42398300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>4.19</td>\n",
              "      <td>4.25</td>\n",
              "      <td>4.11</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>42932100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>4.23</td>\n",
              "      <td>4.26</td>\n",
              "      <td>4.14</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>30678700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 15,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.describe()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "data": {
            "text/plain": [
              "              Open         High          Low        Close    Adj Close  \\\n",
              "count  1172.000000  1172.000000  1172.000000  1172.000000  1172.000000   \n",
              "mean      7.013959     7.161143     6.868012     7.015776     7.015776   \n",
              "std       4.880564     4.985040     4.779099     4.887464     4.887464   \n",
              "min       1.620000     1.690000     1.610000     1.620000     1.620000   \n",
              "25%       2.710000     2.780000     2.660000     2.707500     2.707500   \n",
              "50%       4.250000     4.350000     4.175000     4.275000     4.275000   \n",
              "75%      11.585000    11.785000    11.310000    11.555000    11.555000   \n",
              "max      24.940001    27.299999    24.629999    25.260000    25.260000   \n",
              "\n",
              "             Volume  \n",
              "count  1.172000e+03  \n",
              "mean   3.785747e+07  \n",
              "std    3.456219e+07  \n",
              "min    0.000000e+00  \n",
              "25%    1.308202e+07  \n",
              "50%    2.900380e+07  \n",
              "75%    5.064272e+07  \n",
              "max    3.250584e+08  "
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1172.000000</td>\n",
              "      <td>1172.000000</td>\n",
              "      <td>1172.000000</td>\n",
              "      <td>1172.000000</td>\n",
              "      <td>1172.000000</td>\n",
              "      <td>1.172000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.013959</td>\n",
              "      <td>7.161143</td>\n",
              "      <td>6.868012</td>\n",
              "      <td>7.015776</td>\n",
              "      <td>7.015776</td>\n",
              "      <td>3.785747e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.880564</td>\n",
              "      <td>4.985040</td>\n",
              "      <td>4.779099</td>\n",
              "      <td>4.887464</td>\n",
              "      <td>4.887464</td>\n",
              "      <td>3.456219e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.620000</td>\n",
              "      <td>1.690000</td>\n",
              "      <td>1.610000</td>\n",
              "      <td>1.620000</td>\n",
              "      <td>1.620000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.710000</td>\n",
              "      <td>2.780000</td>\n",
              "      <td>2.660000</td>\n",
              "      <td>2.707500</td>\n",
              "      <td>2.707500</td>\n",
              "      <td>1.308202e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.250000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>4.175000</td>\n",
              "      <td>4.275000</td>\n",
              "      <td>4.275000</td>\n",
              "      <td>2.900380e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>11.585000</td>\n",
              "      <td>11.785000</td>\n",
              "      <td>11.310000</td>\n",
              "      <td>11.555000</td>\n",
              "      <td>11.555000</td>\n",
              "      <td>5.064272e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>24.940001</td>\n",
              "      <td>27.299999</td>\n",
              "      <td>24.629999</td>\n",
              "      <td>25.260000</td>\n",
              "      <td>25.260000</td>\n",
              "      <td>3.250584e+08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 16,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Explore Features"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "features = dataset[['Open','High','Low','Close','Adj Close','Volume']]\n",
        "features.head(9)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 17,
          "data": {
            "text/plain": [
              "            Open  High   Low  Close  Adj Close    Volume\n",
              "Date                                                    \n",
              "2014-01-02  3.85  3.98  3.84   3.95       3.95  20548400\n",
              "2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200\n",
              "2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300\n",
              "2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100\n",
              "2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700\n",
              "2014-01-09  4.20  4.23  4.05   4.09       4.09  30667600\n",
              "2014-01-10  4.09  4.20  4.07   4.17       4.17  20840800\n",
              "2014-01-13  4.19  4.20  4.09   4.13       4.13  22856100\n",
              "2014-01-14  4.14  4.30  4.13   4.30       4.30  42434800"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
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              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>3.85</td>\n",
              "      <td>3.98</td>\n",
              "      <td>3.84</td>\n",
              "      <td>3.95</td>\n",
              "      <td>3.95</td>\n",
              "      <td>20548400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>3.98</td>\n",
              "      <td>4.00</td>\n",
              "      <td>3.88</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>22887200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>4.01</td>\n",
              "      <td>4.18</td>\n",
              "      <td>3.99</td>\n",
              "      <td>4.13</td>\n",
              "      <td>4.13</td>\n",
              "      <td>42398300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>4.19</td>\n",
              "      <td>4.25</td>\n",
              "      <td>4.11</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>42932100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>4.23</td>\n",
              "      <td>4.26</td>\n",
              "      <td>4.14</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>30678700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-09</th>\n",
              "      <td>4.20</td>\n",
              "      <td>4.23</td>\n",
              "      <td>4.05</td>\n",
              "      <td>4.09</td>\n",
              "      <td>4.09</td>\n",
              "      <td>30667600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-10</th>\n",
              "      <td>4.09</td>\n",
              "      <td>4.20</td>\n",
              "      <td>4.07</td>\n",
              "      <td>4.17</td>\n",
              "      <td>4.17</td>\n",
              "      <td>20840800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-13</th>\n",
              "      <td>4.19</td>\n",
              "      <td>4.20</td>\n",
              "      <td>4.09</td>\n",
              "      <td>4.13</td>\n",
              "      <td>4.13</td>\n",
              "      <td>22856100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-14</th>\n",
              "      <td>4.14</td>\n",
              "      <td>4.30</td>\n",
              "      <td>4.13</td>\n",
              "      <td>4.30</td>\n",
              "      <td>4.30</td>\n",
              "      <td>42434800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 17,
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "target = dataset['Adj Close']\n",
        "target.head(9)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 18,
          "data": {
            "text/plain": [
              "Date\n",
              "2014-01-02    3.95\n",
              "2014-01-03    4.00\n",
              "2014-01-06    4.13\n",
              "2014-01-07    4.18\n",
              "2014-01-08    4.18\n",
              "2014-01-09    4.09\n",
              "2014-01-10    4.17\n",
              "2014-01-13    4.13\n",
              "2014-01-14    4.30\n",
              "Name: Adj Close, dtype: float64"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 18,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plot Each Features"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "viz = dataset[['Open','High','Low','Close','Adj Close','Volume']]\n",
        "viz.hist()\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 27,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(features.Open, target,  color='blue')\n",
        "plt.xlabel(\"Open\")\n",
        "plt.ylabel(\"Adj Close\")\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              "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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 20,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(features.High, target,  color='blue')\n",
        "plt.xlabel(\"High\")\n",
        "plt.ylabel(\"Adj Close\")\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              "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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 21,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(features.Low, target,  color='blue')\n",
        "plt.xlabel(\"Low\")\n",
        "plt.ylabel(\"Adj Close\")\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              "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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 22,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(features.Close, target,  color='blue')\n",
        "plt.xlabel(\"Close\")\n",
        "plt.ylabel(\"Adj Close\")\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              "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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 23,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(features.Volume, target,  color='blue')\n",
        "plt.xlabel(\"Volume\")\n",
        "plt.ylabel(\"Adj Close\")\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 24,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create Train and Test dataset"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "msk = np.random.rand(len(dataset)) < 0.8\n",
        "train = features[msk]\n",
        "test = features[~msk]"
      ],
      "outputs": [],
      "execution_count": 30,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Simple Regression Model\n",
        "\nTrain Data distribution"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(train['Open'], train['Adj Close'],  color='blue')\n",
        "plt.xlabel(\"Open\")\n",
        "plt.ylabel(\"Adj Close\")\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              "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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 31,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "regr = linear_model.LinearRegression()\n",
        "X_train = np.asanyarray(train[['Open']])\n",
        "y_train = np.asanyarray(train[['Adj Close']])\n",
        "regr.fit (X_train, y_train)\n",
        "# The coefficients\n",
        "print('Coefficients: ', regr.coef_)\n",
        "print('Intercept: ',regr.intercept_)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coefficients:  [[0.99949051]]\n",
            "Intercept:  [0.0039706]\n"
          ]
        }
      ],
      "execution_count": 32,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(train.Open, train['Adj Close'],  color='blue')\n",
        "plt.plot(X_train, regr.coef_[0][0]*X_train + regr.intercept_[0], '-r')\n",
        "plt.xlabel(\"Open\")\n",
        "plt.ylabel(\"Adj Close\")"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 34,
          "data": {
            "text/plain": [
              "Text(0,0.5,'Adj Close')"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 34,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "\n",
        "X_test = np.asanyarray(test[['Open']])\n",
        "y_test = np.asanyarray(test[['Adj Close']])\n",
        "y_pred = regr.predict(X_test)\n",
        "\n",
        "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(y_pred - y_test)))\n",
        "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((y_pred - y_test) ** 2))\n",
        "print(\"R2-score: %.2f\" % r2_score(y_pred , y_test))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean absolute error: 0.14\n",
            "Residual sum of squares (MSE): 0.06\n",
            "R2-score: 1.00\n"
          ]
        }
      ],
      "execution_count": 35,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "version": "3.5.5",
      "pygments_lexer": "ipython3",
      "codemirror_mode": {
        "version": 3,
        "name": "ipython"
      },
      "name": "python",
      "nbconvert_exporter": "python",
      "mimetype": "text/x-python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "nteract": {
      "version": "0.12.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}